package com.atguigu.day12;

import com.atguigu.bean.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple1;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.annotation.DataTypeHint;
import org.apache.flink.table.annotation.FunctionHint;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.functions.ScalarFunction;
import org.apache.flink.table.functions.TableFunction;
import org.apache.flink.types.Row;

import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.call;

public class Flink07_SQL_UDF_TableFun {
    public static void main(String[] args) {
        //1.获取流的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setParallelism(1);

        //2.获取表的执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        //3.从端口读取数据并转为WaterSensor
        SingleOutputStreamOperator<WaterSensor> streamSource = env.socketTextStream("localhost", 9999)
                .map(new MapFunction<String, WaterSensor>() {
                    @Override
                    public WaterSensor map(String value) throws Exception {
                        String[] split = value.split(",");
                        return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
                    }
                });

        //4.将流转为表
        Table table = tableEnv.fromDataStream(streamSource);

        tableEnv.createTemporaryView("sensor",table);

        //不注册直接使用（只能在TableAPI中使用）
      /*  table.joinLateral(call(MyUDTF.class,$("id")))
                .select($("id"),$("result"))
                .execute()
                .print();   */
        //可以对炸出来的侧写表中的字段进行重命名
/*        table.joinLateral(call(MyUDTF.class,$("id")).as("result"))
//                .select($("id"),$("f0"))
                .select($("id"),$("result"))
                .execute()
                .print();*/

        //先注册再使用
        tableEnv.createTemporarySystemFunction("myUdtf",MyUDTF.class);
/*        table.joinLateral(call("myUdtf",$("id")).as("result"))
//                .select($("id"),$("f0"))
                .select($("id"),$("result"))
                .execute()
                .print();*/
        //SQL中使用
  /*      tableEnv.executeSql(
                "select " +
                        "id," +
                        "f0 as aa " +
                        "from sensor , lateral table(myUdtf(id))").print();*/
              tableEnv.executeSql(
                "select " +
                        "id," +
                        "aa " +
                        "from sensor left join lateral table(myUdtf(id)) as T(aa) on true").print();

    }

    //TODO 自定义一个表函数，一进多出 ，输入id然后根据下划线切分，将切分后的结果返回出来
/*    @FunctionHint(output = @DataTypeHint("ROW<result String>"))
    public static class MyUDTF extends TableFunction<Row>{
        public void eval(String value){
            String[] strings = value.split("_");
            for (String string : strings) {
                collect(Row.of(string));
            }
        }
    }  */


    public static class MyUDTF extends TableFunction<Tuple1<String>>{
        public void eval(String value){
            String[] strings = value.split("_");
            for (String string : strings) {
                collect(Tuple1.of(string));
            }
        }
    }

}
